As businesses become more customer centric, it has never been more urgent than today to leverage the connections in your data to make timely operational decisions. This requires a technology to unify your master data, including customer, product, supplier and logistics information to power the next generation of ecommerce, fraud detection, supply chain and logistics applications.
Neo4j enables the creation of a 360° view of your master data made available in real time to all your operational applications. Such a 360° view can be created either by managing all your master data inside a single repository or by creating a shared metadata repository to support activities ranging from ecommerce to customer support.
Your Master Data Is a Graph: Are You Ready?
Learn why your master data is already a graph and how graph databases like Neo4j are the best technology fit for MDM.Read the white paper
Better Insights from Your Master Data
Watch this demo from the Graph Database LA Meetup as they discuss master data management.Watch the video
Graph Databases in the Enterprise: Master Data Management
Discover how to tap into the power of graph databases to organize and manage your master data with a flexible and schema-free database model.Read more
Gain real-time business insights from relationships in master data when storing and modeling data as a graph, including queries around data ownership, organizational hierarchies, human capital management, supply chain transparency and a 360° view of your customers for customized, up-to-the minute marketing, sales and support.
Empowered business decisions
Graphs give MDM professionals the insights needed to acquire and retain more customers, accelerate time-to-value from acquisitions and deliver better products and services.
Faster time to market
The needs of your master data application change as often as your customers. The graph data model can be seamlessly evolved and built upon to accommodate new data sources and types, so your application can be adjusted with incredible agility as your customer and data needs change.
Complex and hierarchical datasets
Master data, such as organizational and product data, has deep hierarchies with top-down, lateral and diagonal connections. Managing such data models with a relational database results in complex and unwieldy code that is slow to run, expensive to build and time-consuming to maintain.
Real-time query performance
Master data systems must integrate with and provide data to a host of applications within the enterprise – often in real time. However, traversing a complex and highly interconnected data set to provide real-time information is a challenge without the right technology.
Master data is highly dynamic, with the constant addition and re-organization of nodes, making it harder for your developers to design systems that accommodate both current and future requirements.
Native graph storage
Unlike relational databases, Neo4j stores interconnected master data that is neither purely linear nor hierarchical. Neo4j’s native graph storing makes it easier to decipher your data by not forcing intermediate indexing at every turn.
Neo4j’s versatile property graph model makes it easier for organizations to evolve master data models as customer needs and data sources, types and formats change over time.
Performance and scalability
Neo4j’s native graph processing engine supports high-performance graph queries on large master datasets to enable real-time decision making no matter how fast or large your data grows.
The built-in, high-availability features of Neo4j ensure your master data is always available to mission-critical applications, and ACID-compliant transactions ensure the integrity of your master data for always-accurate customer and product insights.
Discover how Schleich – one of the largest toy manufacturers in Germany – uses Neo4j for end-to-end supply chain management in a way that is fast, flexible and easy to use for all stakeholders.Read more
Creating Business Value through Data Relationships
Where does sustainable competitive advantage come from? It’s not from data volume or velocity, but from the knowledge of relationships in your data.Download Now